1. Field of the Invention
The present invention relates to a revolution indicator and a program for a revolution indicator which measures the number of revolutions of a measuring object performing a revolving movement.
2. Related Art
The number of revolutions (revolution speed) of a measuring object performing the revolving movement is measured by mounting such a revolution indicator as rotary encoder and the like.
However, since such a revolution indicator must be directly mounted to the measuring object, there have been problems; in which a jig must sometimes be fabricated for mounting the revolution indicator, and the effect(s) of the mounted revolution indicator end up changing the revolving movement properties of the measuring object, thus making it sometimes impossible to measure precisely the number of revolutions of the measuring object.
Hence, revolution indicators which can measure the number of revolutions of the measuring object without any adverse effects have been introduced and provided in open markets (for an example, refer to Japanese Unexamined Patent Application Publication No. H02-21266).
This invention detects the magnetic leakage flux of a revolving motor which is the object of measurement, finds the spectrum (analytical data) of the leakage flux by the fast Fourier transform (FFT) and finds the number of revolutions by determining the frequency showing the maximum value of the spectrum to be the revolution frequency of the revolving motor.
However, since revolution indicators such as these compute analytical data by FFT at a specific number of sampling points, one problem has been that the response of the analytical data to the variation of the number of revolutions is low, causing the lowering of the reliability of the analytical data.
In regard to this, counter-measures have been considered, such as reducing the number of sampling points and the like. However, the resolution of the frequency becomes coarse, which sometimes lowers the reliability of the computed analytical data.
For example, when the FFT computation is performed at 1024 sampling points within the measuring range of 500 Hz, a data length of 0.8 seconds is necessary, and the frequency resolution of the spectrum becomes 1.25 Hz.
On the other hand, when the FFT computation is performed at 256 sampling points within the same range (500 Hz), a data length of 0.2 seconds is necessary, and the frequency resolution of the spectrum becomes 5 Hz.
When both of these are compared there are not many sampling points, it is easy to respond to variations in the number of revolutions, since the data length of the data can be managed within a short amount of time. However, due to the lowering of the frequency resolution, the accuracy of the analytical data becomes coarse.
On the Contrary, when there are several sampling points, the accuracy of the analytical data is enhanced, since the frequency resolution is more detailed. However, since the data length of the data is lengthened, it is hard to respond to variations in the number of revolutions, which sometimes makes it difficult to determine the maximum value of the spectrum, and sometimes even lowers the measuring accuracy of the number of revolutions when varying.